Literature DB >> 29761737

Predictive validity of NEDA in the 16- and 21-year follow-up from the pivotal trial of interferon beta-1b.

Douglas S Goodin1, Anthony T Reder2, Anthony L Traboulsee3, David Kb Li3, Dawn Langdon4, Gary Cutter5, Stuart Cook6, Timothy O'Donnell7, Marcelo Kremenchutzky8, Joel Oger9, Ralf Koelbach10, Christoph Pohl11, Eva-Maria Wicklein12.   

Abstract

BACKGROUND: Long-term follow-up from the randomized trial of interferon beta-1b (IFNB-1b) permitted the assessment of different definitions of no evidence of disease activity (NEDA) for predicting long-term outcome in multiple sclerosis (MS).
OBJECTIVE: To examine the predictive validity of different NEDA definitions.
METHODS: Predictive validity for negative disability outcomes (NDOs) at 16 years and survival at 21 years post-randomization were assessed. NEDA in the first 2 years was defined as follows: clinical NEDA: no relapses or Expanded Disability Status Scale (EDSS) progression from baseline to Year 2; NEDA-3a: no relapses, no confirmed ⩾1-point EDSS progression, and no new T2-active lesions; NEDA-3b: no relapses, no EDSS progression, and no increase in T2 burden of disease (T2-BOD); and NEDA-4: no relapses, no EDSS progression, and no increase in T2-BOD or atrophy. NDOs were defined as death, need for wheelchair, EDSS ⩾6, or progressive MS.
RESULTS: A total of 245 and 371 patients were evaluated at 16 and 21 years, respectively. Clinical NEDA predicted NDOs ( p = 0.0029), as did baseline EDSS ( p < 0.0001), baseline T2-BOD ( p < 0.0001), and change in T2-BOD ( p = 0.0033). IFNB-1b treatment ( p = 0.0251), relapse rate in the 2 years before study start ( p = 0.0260), T2-BOD at baseline ( p = 0.0014), and change in T2-BOD ( p = 0.0129) predicted survival at 21 years.
CONCLUSION: Clinical NEDA predicted long-term disability outcome. By contrast, definitions of NEDA that included on-therapy changes in magnetic resonance imaging variables did not increase the predictive validity.

Entities:  

Keywords:  Multiple sclerosis; autoimmune diseases; interferon beta-1b; prognosis

Mesh:

Substances:

Year:  2018        PMID: 29761737     DOI: 10.1177/1352458518773511

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  6 in total

1.  Assessing the Metabolomic Profile of Multiple Sclerosis Patients Treated with Interferon Beta 1a by 1H-NMR Spectroscopy.

Authors:  Lorena Lorefice; Federica Murgia; Giuseppe Fenu; Jessica Frau; Giancarlo Coghe; Maria Rita Murru; Stefania Tranquilli; Andrea Visconti; Maria Giovanna Marrosu; Luigi Atzori; Eleonora Cocco
Journal:  Neurotherapeutics       Date:  2019-07       Impact factor: 7.620

2.  The effectiveness of interferon beta versus glatiramer acetate and natalizumab versus fingolimod in a Polish real-world population.

Authors:  Katarzyna Kapica-Topczewska; Joanna Tarasiuk; Francois Collin; Waldemar Brola; Monika Chorąży; Agata Czarnowska; Mirosław Kwaśniewski; Halina Bartosik-Psujek; Monika Adamczyk-Sowa; Jan Kochanowicz; Alina Kułakowska
Journal:  PLoS One       Date:  2019-10-24       Impact factor: 3.240

3.  Matching comparisons of therapeutic efficacy suggest better clinical outcomes for patients treated with peginterferon beta-1a than with glatiramer acetate.

Authors:  Thomas F Scott; Ray Su; Kuangnan Xiong; Arman Altincatal; Carmen Castrillo-Viguera; Maria L Naylor
Journal:  Ther Adv Neurol Disord       Date:  2021-01-12       Impact factor: 6.570

Review 4.  Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.

Authors:  Ruggiero Seccia; Silvia Romano; Marco Salvetti; Andrea Crisanti; Laura Palagi; Francesca Grassi
Journal:  Life (Basel)       Date:  2021-02-05

5.  Association of NEDA-4 With No Long-term Disability Progression in Multiple Sclerosis and Comparison With NEDA-3: A Systematic Review and Meta-analysis.

Authors:  Dalia Rotstein; Jacqueline M Solomon; Maria Pia Sormani; Xavier Montalban; Xiang Y Ye; Dina Dababneh; Alexandra Muccilli; Georges Saab; Prakesh Shah
Journal:  Neurol Neuroimmunol Neuroinflamm       Date:  2022-10-12

6.  Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

Authors:  Andrea Tacchella; Silvia Romano; Michela Ferraldeschi; Marco Salvetti; Andrea Zaccaria; Andrea Crisanti; Francesca Grassi
Journal:  F1000Res       Date:  2017-12-22
  6 in total

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